Unsupervised self-training method based on deep learning for soil moisture estimation using synergy of sentinel-1 and sentinel-2 images. (2nd January 2023)

• Record Type: Journal Article
• Authors: Ben Abbes, Ali; Jarray, Noureddine
• Is Part Of: International journal of image and data fusion. Volume 14:Number 1(2023)
• Page Start: 1
• Page End: 14
• Publication Date: 2023-01-02
• Subjects: Convolutional neural network -- unsupervised learning -- Self-Training -- soil moisture -- fusion -- sentinel-1A -- sentinel-2A; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2022.2106317
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: Here, we present a novel unsupervised self-training method (USTM) for SM estimation. First, a ML model is trained using the labeled and unlabeled data. Then, the pseudo-labeled data are generated employing the second model by adding a proxy labeled data. Eventually, SM is estimated applying the third model by pseudo-labeled data generated by the second model and unlabeled data. The final SM estimation result is obtained by training the third model. Subsequently, in-situ measurements are performed to validate our method. The final model is an unsupervised learning model. Experiments were carried out at two different sites located in southern Tunisia using Sentinel-1A and Sentinel-2A data. The input data include the backscatter coefficient in two-mode polarization ( σ ° VV and σ ° VH ), derived from Sentinel-1A, as well as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Infrared Index (NDII) for Sentinel-2A and in-situ data. The USTM method based on (Random Forest (RF)- Convolutional neural network (CNN)-CNN) combination allowed obtaining the best performance and precision rate, compared to other combinations (Artificial Neural Network (ANN)-CNN-CNN) and (eXtreme Gradient Boosting (XGBoost)-CNN-CNN).